However, bacterial asexual reproduction is generally more similar to manuscript copying than mammalian sexual reproduction.

both tasks could thus profit from each other provided they are understood as separate and developed each in its own right.


This distributed representation can inform an inductive bias to generalize in a bootstrapping system.

5 labeling entities solely based on similarity scores resulted in lower performance.


Both our methods outperform the baseline and the interpolation approach.

this combination process is referred to as triangulation (see §5).


We argue that our joint training procedure can be seen as optimizing the posterior likelihood of the three models.

we argue that our joint training procedure can be seen as optimizing the posterior likelihood of the three models.


Based on the above analysis, we first identify key-phrases from short text, which can be deemed as self-contained knowledge, then propose phrase topic model (PTM), which constrains same topic for terms in key-phrase and sample topics for non-phrase terms from mixture of keyphrase's topic.

the hidden variables consist of z m,n and δ m,s .


vast amount of lexical knowledge about words and their relationships, denoted as LR-sets, available in online dictionaries or other resources can be exploited by this model to generate more coherent topics.

bTM learns topics over short texts by modeling the generation of biterms in the whole corpus.


As seen in Figure 1, the word embedding of the word table is more closer to the centroid C 2 as compared to the centroids C 1 and C 3 .

this is also justified when we consider only synset members, gloss members, hypernym/hyponym synset members, hypernym/hyponym gloss members which give a score close to the best obtained score.


The other reason is to bring down the impact of topic drift which may have occurred because of polysemous synset members.

due to time and space constraints we have performed our experiments on only Hindi and English languages.


This data set consists of 10017 sentences and nine types of relations between nominals (Hendrickx et al., 2010).

recursive Neural Network (rNN) has proven to be highly successful in capturing semantic compositionality in text and has improved the results of several Natural Language Processing tasks (Socher et al., 2012;Socher et al., 2013).


We use dictionaries extended with Brown clusters to collect labeled training data from unlabeled data, saving additional annotation work.

for evaluation, we use three domains: tweets, spoken data and queries.


For evaluation, we use three domains: tweets, spoken data and queries.

we mine for sequences of unambiguous tokens in a structured prediction task.


It is thus verified that density peaks clustering algorithm is able to handle MDS effectively.

we further revise the clustering algorithm to address the summary length constraint.


In this work, we measure diversity in the ranking model.

we define the following function to calculate the representativeness score s REP (i) for each sentence s i : where sim ij denotes the similarity value between the i-th and j-th sentence, and K denotes the number of sentences in the datasets.


In addition, we used large English-Italian and English-Portuguese bilingual lexicons available from FreeLang site (http://www.freelang.net/dictionary) as well as an English-Chinese bilingual word list available from LDC (Linguistic Data Consortium).

in this paper, we report on an experiment to develop prototype semantic annotation tools for italian, Chinese and Brazilian Portuguese based on an existing English annotation tool.


In this paper, we have investigated the feasibility of rapidly bootstrapping semantic annotation tools for new target languages 2 by mapping an existing semantic lexicon and software architecture.

bank as river bank vs. money bank), translation errors and missing of the translation words in the English semantic lexicons.


We can expect that these two texts are related but the similarity value does not reflect that.

then we identified the concepts that appear both in the document ESA representation and in the label ESA representation.


Note that this similarity is not symmetric.

in addition to the original documents, we also split each document into 2, 4, 8, 16 equal length parts, computed the ESA representation of each, and then the intersection with the ESA representation of the label.


We can think about S A (x, y) as leveraging term many-to-many mapping, while  S M (x, y) uses only one-to-many term mapping.

we propose to align different indices of x and y together to increase the similarity value.


In September 2014, Twitter users unequivocally reacted to the Ray Rice assault scandal by unleashing personal stories of domestic abuse via the hashtags #WhyIStayed or #WhyILeft.

the vast majority of cases can be classified accurately with ngrams alone.


To the best of our knowledge, no previous word-embedding techniques have attempted to incorporate morphological tags into embeddings in a supervised fashion.

we conducted experiments on the TIGER corpus of newspaper German (Brants et al., 2004).


Our framework supports determination of various important Social Constructs such as Leadership, Status, Group Cohesion and Sub-Group Formation.


The Structured Skip-gram and CWindow models can process 34.64k and 124.43k words per second, respectively.

when defining a window size of 5, the actual window size used for each sample is a random value between 1 and 5.


The left-most point (α = 0) for each figure corresponds to simple regression.

the knowledge on numerical attributes is also very useful on many other occasions.


Sometimes sellers are encouraged to find similar products to those they sell and adopt this category to their products.

we conducted an empirical evaluation on 445, 408 product titles and used a rich product taxonomy of 319 categories organized into 6 levels.


Such process will both alleviate human labor and further improve product categorization consistency in e-Commerce websites.

this mechanism leads to two main problems: (1) it takes a lot of time for a merchant to categorize items and (2) such taggings can be inconsistent since different sellers might categorize the same product differently.


The most straightforward strategy to perform model selection for the task of response-based learning for SMT is to rely on parsing evaluation scores that are standardly reported in the literature.

statistical significance is measured using an Approximate Randomization test (Noreen, 1989;Riezler and Maxwell, 2005).


Indeed, the response generated by applying SMT to the most recent stimulus "What?"

grammaticality of the output is handled by the language model, and the language model is constructed upon the target language only, which in our case corresponds to the target utterances that remain untouched.


If there are multiple phrase-pairs in P that correspond to the same target phrase phr t , we select the shortest source phrase (phr s ).


(2014a) propose IAA-weighted costsensitive learning for POS tagging.

we use the doubly-annotated data to regularize our model, hopefully preventing overfitting to annotators' biases.


This might be due to: (a) our use of @-mention features; (b) l 1 regularisation, which is essential to preventing overfitting for large feature sets; or (c) our use of l 2 normalisation of rows in the design matrix, which we found reduced errors by about 20% on GEOTEXT, in keeping with results from text categorisation (Lee, 1995).

there is no clear consensus on whether text-or network-based methods are empirically superior at the user geolocation task.


Jurgens (2013) defined an undirected network from interactions among Twitter users based on @-mentions in their tweets, a mechanism typically used for conversations between  friends.

we thank the anonymous reviewers for their insightful comments and valuable suggestions.


Section 5 presents experimental setup and results.

it comes up with a new scheme TF-KLD-KNN to learn the discriminative weights of words and phrases specific to paraphrase task, so that a weighted sum of embeddings can represent sentences more effectively.


The key idea in FCM is that it gives similar words (i.e.

for each instance (y, x) we compute the gradient of the log-likelihood = log P (y|x; T, W f ).


Instead of having a target word with different senses, we included the target word in each sense, and we kept a list of unique senses, including for each word its frequency in the Web using a large search engine index.

hence, we use it here as the stateof-the-art in our evaluation.


See two possible contexts for noche and fortuna in the examples below: era una noche oscura de ('it was a dark night of') de probar fortuna en el ('to try fortune in the') Third, we define the complexity of a word using the relative frequency of the synonyms within the same sense in the List of Senses.

our method improves upon LexSiS and the baseline for all the measures.


We also compare our results to those reported by Berg-Kirkpatrick et al.

in the process, the model learns to cluster words into soft equivalence classes (words that have similar distributions).


Regarding Twitter sentiment analysis, the top performing system  from Semeval-2013 Twittter Sentiment Analysis task (Nakov et al., 2013) follows this recipe by training an SVM on various surface form, sentiment and semantic features.

with more than 40 systems participating in Semeval-2014 challenge, we note that the majority of systems perform well only on few test sets at once while failing on the others 5 .


While the idea to model statistical correlations between the words and tweet labels using PMI or any other metric is rather intuitive, we believe there is a more effective way to exploit noisy labels for estimating the word-sentiment association scores.

(Tang et al., 2014) showed that learning sentiment-specific word embeddings and using them as features can boost the accuracy of existing sentiment classifiers.


The influence of this head-direction parameter on English acquisition has been previously investigated (Flynn, 1989).

frequencies of 400 English function words 3 are extracted as features.


In order to align the CAREFUL SCOTUS and ORIGINAL OYEZ transcripts, we use a dynamic programming algorithm for sequence alignment with matching scores as given in Where there used to be the decision The ANNOTATED OYEZ training set is a very small dataset, and other work has shown that Switchboard (SWBD) is useful for cross-domain training for SCO-TUS (Zayats et al., 2014).

in this work, we use a simple self-training approach.


The authors also thank Sangyun Hahn for his contribution in the two-stage model.

those transcripts are identical to the original OYEZ transcripts, but in addition contain disfluency annotation derived from CARE-FUL SCOTUS.


This time for word "state": state : e→t.

for evaluation, we follow Zettlemoyer and Collins (2005)  Recall = # of correctly parsed questions # of questions .


Several works in literature (Zettlemoyer and Collins, 2005;Zettlemoyer and Collins, 2007;Wong and Mooney, 2007;Kwiatkowski et al., 2013) employ some primitive type hierarchies and parse with typed lambda calculus.

in its outermost lambda abstraction, variable P needs to be grounded on-the-fly before we push the expression onto the stack.


Semantic parsing has made significant progress, but most current semantic parsers are extremely slow (CKY-based) and rather primitive in representation.

argmax is a polymorphic function, and to assign a correct type for it we have to introduce type variables: where type variable 'a is a place-holder for "any type".


We next show how to use the template kernels within a reranker.

each slot is associated with a set of properties.


A key observation to make is that the v generated by the PA algorithm will depend on two parameters.

to previous works on parsing with kernels (Collins and Duffy, 2002), in which the kernels are defined over trees and count the number of shared subtrees, our focus is on feature combinations.


Table 2 shows the result of this search, and again the result is very useful.

these two ideas are stated as an optimization problem where the first becomes the objective and the second a constraint.


It uses two ideas: first, that vectors for polysemous words can be decomposed into a convex combination of sense vectors; secondly, that the vector for a sense is kept similar to those of its neighbors in the network.

a very large number of words are monosemous, and the procedure will leave the embeddings of these words unchanged.


Characterizing verbs on the personal vs. nonpersonal dimension indeed turned out to beneficial for explaining domain-level importance of verbs in world news: personal narratives are not considered important in this domain and verbs that tended to get excluded from summaries also tended to appear more frequently in personal blog entries.

the goal is to collect evidence of verb importance globally, without regard to a particular input or its context.


The first rule indicates that a person is acting as an intermediary in the transaction.

it aims to provide an open access to electronic documentation of ancient cuneiform, consisting of texts, images, transliterations and glossaries of 3500 years of human history.


The classifier with Web Context Features achieved an F score > 70% using only 10 training texts, and approached its best performance with just 100 training texts.

to our knowledge, classifying medication mentions with respect to administration use categories has not yet been studied.


The second group of approaches performs taxonomy induction to learn hypernymy relation-ships between words (Moro and Navigli, 2012;Meyer and Gurevych, 2012).

a new synset is created for that lemma and a hypernym relation is added to the appropriate WordNet synset.


2013, ADW, 3 which first represents a given linguistic item (such as a word or a concept) using random walks over the WordNet semantic network, where random walks are initialized from the synsets associated with that item.

structurebased approaches are limited only to the concepts appearing in Wikipedia article titles, which almost always correspond to noun concepts.


The corpora NEWS contains news articles in English and  : Shows the break-even point (f1-score) of the proposed method Co and three baselines for each pair of corpora.

text classification hugely relies on manually annotated training data in one language.


Others argued that al-Assad's actions were a threat to regional security, also argu-ing for military action.

we excluded sources outside the US (e.g., BBC), news aggregators (e.g., Yahoo News, Google News), blogs (Huffington Post), and sites without a dedicated politics feed (e.g., USA Today, weather.com).


Framing is often instantiated by specific "keywords, stock phrases," (Entman, 1993, p. 52) or "catchphrases" (Gamson and Modigliani, 1989, p. 3).

for example, "depictions," "visual images," and figurative language such as metahpor (Gamson and Modigliani, 1989) often invoke frames.


The results above suggest that, when included, such features also emerge as important for identifying the language of framing.

bias involves a clear, often intentional, preference shown in writing for one position or opinion on an issue.


We did experiment with different post-hoc decision thresholds to make the classifier less aggressive.

moreover, this precision-recall trade off may have different ramifications in different applications.


Imagery & Context Imagery rating of the word and average imagery rating of its context (Paivio et al., 1968;van der Veur, 1975).

the words and phrases the classifier highlights should align with that population's perception of framing.


As a next step, we conducted another analysis on the distribution of "extreme cases", i.e.

finally, the average homogeneity scores are significantly (p < .001) higher in persuasive sentences in all datasets except CORPS, where the scores of non-persuasive sentences are significantly higher (p < .01) than persuasive ones.


Another known problem with RNNs is that, it can be difficult to train them to learn longrange dependencies (Hochreiter et al., 2001).

the dataset comes with several human generated descriptions in a number of languages; we use the roughly 40 available English descriptions per video.


Our approach follows the general paradigm of SMT with two important differences.

the value of the term is maximum when the selected objects are ranked first.


(ii) For the first time, we apply maximum expected BLEU training on a data set as large as four million sentence pairs.

due to the renormalization component, this results in a total of 6.08M features that are updated with GT using the same data.


In higher iterations, the update steps for good features keep growing and we observe an exponential increase of the objective function.

the triplet features did not finish in time, so we applied the feature sets (a), (b) and (d), 45M features in total.


Some rare words do not receive a vector representation after running Word2Vec, and we simply remove phrases containing those words, resulting in a total of 0.65m phrases for Arabic, 0.18m phrases for Urdu, and 1.2m phrases for English.

the algorithms presented in the previous section require rapid retrieval of neighboring phrases in continuous space.


2014, we use a baseline MT system to translate the Arabic or Urdu phrases and add their translations to the English phrase set.

this extra computation takes only a negligible amount of time, since the number of labeled phrases on the source side is significantly smaller than the number of phrases on the target side.


), but are more general, with semantic categories relevant to common nouns and verbs as well.

here we build on prior work with an inventory of semantic classes (for nouns and verbs) known as supersenses.


In addition to an overwhelming task of trying to capture all words and expressions that can convey a sentiment there are many other problems to solve: resolving the scope of negation to determine the shift of polarity (Lapponi et al., 2012), determining if an opinion is present in interrogative or conditional sentences (Narayanan et al., 2009), dealing with irony (Tsur, 2010), etc.

the recall for the positive class is substantially low than for the negative class.


Our next technique (Frequency Method) modifies the phrase table by assigning short bit codes to frequent words, and long bit codes to infrequent words.

"Recalls" indicates how many participants returned to type their memorized English sequences, and "Correct Recalls" tells how many sequences were accurately remembered.


A random, computergenerated 60-bit string is much more secure.

instead of a 2048-word dictionary, we use a 32,7868-word dictionary.


Feature types are shared across categories, e.g., categories CLOTHING (k1), BIRDS (k2), and FOOD (k3) are all associated with feature type color (g2).

we compared the performance of BCF against BayesCat, a Bayesian model of category acquisition (Frermann and Lapata, 2014) and Strudel, a pattern-based model which extracts concept features from text (Baroni et al., 2010).


Both n-gram factors will therefore be included as fixed effects and as by-subject random slopes in the baselines of the remaining evaluations in this study.

it is common for psycholinguistic models to include a measure of n-gram predictability for each fixated word conditioned on its context, but unless probabilities for words between fixations are also included, the probabilities used in this calculation are   Table 1, the standard bigram factor (top line) predicts that the reading time of the region that ends with word 6 depends on word 5, but the probability of word 5 given its context is never included in the model, so an improbable transition between words 4 and 5 would not be caught.


Qualitative evaluation shows that the model makes reasonable predictions of the level of formality of social network ties in well-known movies.

to compute the predictive log-likelihood of the address terms, we hold out a randomly-selected 10% of films.


In contrast, a status-based network theory would penalize non-transitive triads such as β >>< .

this is a natural next step from prior work that computes the frequency of triads in explicitly-labeled signed social networks (Leskovec et al., 2010b).


As a result, the morphological analysis happens within a different model compared to the model in which the resulting morphemes are consequently used.

for German, we use the Gur350 and ZG222 datasets (Zesch and Gurevych, 2006).


The proposed method greatly improves the flexibility of translation rules at the cost of only a 30% increase in decoding time, and we demonstrate a 1.2-1.9 BLEU improvement over a strong tree-to-tree baseline.

in this paper we have proposed flexible nonterminals for dependency tree-to-tree translation.


In this section, we propose a second phrase selection method based on the results from the syntactic analysis of source language data.

bold face indicates the highest coverage for each number of additional words.


This method allows for improvement of coverage with fewer additional words than sentence selection, achieving higher efficiency by reducing the amount of data unnecessarily annotated.

the accuracy is conversely degraded if we use only phrase pairs with confidence level 3.


Our empirical research questions are as follows: • can we control the production of honorifics in neural machine translation via side constraints?

our approach is not specific to this architecture.


The actual benefit from this reduction is highly implementation-and architecture-dependent.

recall that c i = e i−1 i−n+1 , f a i +m a i −m .


reviews for a movie, or arguments for a controversial social issue), and then outputs a one-sentence abstractive summary that describes the opinion consensus of the input.

we further constructR to contain all the pairwise differences (r p − r q ).L is a vector of the same size asR with each element as 1.


Since we have three systems we performed this pairwise study thrice.

while clusters that balance the entities are preferable, it is also acceptable to have clusters where one of the entities is sparsely represented (or not represented at all).


Albeit the encouraging performance of our proposed approach on summarizing student responses, when applied to the DUC 2004 dataset (Hong et al., 2014) and evaluated using ROUGE we observe only comparable or marginal improvement over the ILP baseline.

notable systems include maximal marginal relevance (Carbonell and Goldstein, 1998), submodular functions (Lin and Bilmes, 2010), jointly extract and compress sentences (Zajic et al., 2007), optimize content selection and surface realization (Woodsend and Lapata, 2012), minimize reconstruction error (He et al., 2012), and dual decomposition (Almeida and Martins, 2013).


Thesaurus synonyms are not that helpful for generating inference rules (or else we will generate rules like produce → percolate ).

for example, the rule be the president of → be not the president of , will be rejected.


But not all relations are distributive in this sense.

this work has availed itself of increasingly sophisticated features of the semantics of the units to be related (Braud and Denis, 2015); but as the PDtB does not provide full discourse structures for texts, it is not relevant to our concerns here.


For our experiments we used a corpus collected from chats involving an online version of the game The Settlers of Catan described in Afantenos et al., 2015).

for example, we could complicate (2) slightly: ( In 3, the SDRS graph would be: this SDRS entails that a is explained by [c, d] and that b is explained by [c, d].


As decoding proceeds, the influence of the initial input on decoding (i.e., the source sentence representation) diminishes as additional previously-predicted words are encoded in the vector representations.

intuitively, it seems desirable to take into account not only the dependency of responses on messages, but also the inverse, the likelihood that a message will be provided to a given response.


For instance, by using a basic unigrambased definition of discussion points, we do not account for the context or semantic sense in which these points occur.

often the debates are quite tight: for 30% of the debates, the difference between the winning and losing sides' deltas is less than 10%.


This figure is above the average inter-annotator agreement of 0.67, which has been referred to as the ceiling performance in most work up to now.

contrary to the SimLex-999 experiments, starting from the Paragram vectors did not lead to superior performance, which shows that injecting the application-specific ontology is at least as important as the quality of the initial word vectors.


cheaper and pricey) is critical for the performance of dialogue systems.

in our opinion, the average inter-annotator agreement is not the only meaningful measure of ceiling performance.


A new feature of this version is that it assigns relation types to its word pairs.

a dialogue system can be led seriously astray by false synonyms.


The entropy is shown at the bottom of the figure.

moreover, linguistic studies have shown that action verbs such as cut and slice often denote some change of state as a result of the action (Hovav and Levin, 2010;Hovav and Levin, 2008).


Also note that the whole prepositional phrase "from the drawer" is identified as the source rather than "the drawer" alone.

the traditional SRL is not targeted to represent verb semantics that are grounded to the physical world so that artificial agents can truly understand the ongoing activities and (learn to) perform the specified actions.


The focus of our experiments is on metaphorical expressions in verb-subject, verb-direct object and adjectival modifier-noun constructions.

the thesholds appear to be relatively stable, with a standard deviation of 0.03 for MIXLAtE; 0.02 for WORDCOS (linguistic); and 0.05 for PHRASECOS1 (visual).


We then use this English caption to retrieve images using the En-Image CorrNet.

en-Image CorrNet: This is the CorrNet model trained using only Z 1 as defined earlier in this section.


The MSCOCO dataset 2 contains images and their English captions.

(A plate with meat and green veggies mixed with sauce.)


encodes the input v j into a hidden representation h and then g V j (.)

there is no parallel data available between the non-pivot views.


Table 6: Images that were assigned an incorrect sense in the PRED setting.

no comparable resource is available for verbs (see Section 2.1).


Using WordNet we construct a superset G containing all possible parent relations for the relations in S by replacing their arguments o 1 , o 2 by all their possible hypernyms.

we use seven spatial relations and allow natural language relations that represent a larger array of higher level semantics.


Second, it is manipulated using both push and pop operations.

the generation algorithm also requires slightly modified constraints.


Instead, we rely on the LSTM supertagger to implicitly model the dependencies-a task that becomes more challenging with longer dependencies.

(2013) with a shiftreduce parser and a chart-based model.


These models are 0.3 and 1.5 F1 more accurate than the C&C baseline respectively, which is well within the margin of improvement obtained by our model.

parsing models either use these scores directly (Auli and Lopez, 2011b), or as a form of beam search (Clark and Curran, 2007), typically in conjunction with models of the dependencies or derivation.


For each position i, we select the most probable supertag from the output distribution.

we achieve strong accuracies compared to (Wang et al., 2015) using feed-forward neural network model trained on local context, showing that this task does not require bi-LSTMs.


During testing we simply apply the word segmenter to the sentences.

in addition, adapting the word segmentation with NER partiallylabeled data gives a further gain for both CTB6 and PKU, with an F-measure of 86.96% and 87.64% respectively.


), sentences are represented as strings of characters without similar natural delimiters.

from the adaptation the system learns to put a word boundary between '亲' and '四' and then the correct slot value '四季酒店' (four Seasons Hotel) is extracted.


After that the performance seems to quickly saturate.

the joint-learning process generally assumes the availability of manual word segmentations for the training data, which limits the use of this approach.


During training, we use L-BFGS to compute the maximumlikelihood estimates of φ.

• Event trigger: the word or phrase that clearly expresses its occurrence.


In order to capture the inter-dependencies between triggers and argument roles, we introduce memory vectors/matrices to store the prediction information during the course of labeling the sentences.

after that, higher level features has been investigated to improve the performance (Ji and Grishman, 2008;Gupta and Ji, 2009;Patwardhan and Riloff, 2009;Liao and Grishman, 2010;Liao and Grishman, 2011;Hong et al., 2011;McClosky et al., 2011;Huang and Riloff, 2012;Li et al., 2013).


(2015) by means of visualization experiments and cell activation statistics in the context of character-level language modeling.

it is desirable to have a more principled mechanism allowing us to inspect recurrent architectures from a linguistic perspective.


This work was supported by a Google Faculty Research award to the third author.

these two vectors Figure 1: A fragment of our model with latent variable zt, which only illustrates discourse information flow from sentence (t − 1) to t. the information from sentence (t − 1) affects the distribution of zt and then the words prediction within sentence t. are then linearly combined in the generation function for word y t,n , where c t−1 is set to the last hidden state of the previous sentence.


As such, it can be viewed as a form of multi-task learning (Caruana, 1997), where we learn a shared representation that works well for discourse relation prediction and for language modeling.

but because these graphical models represent uncertainty for every element in the model, adding too many layers of latent variables makes them difficult to train.


As a result, the model can act as both a discourse relation classifier and a language model.

unlike this prior work, in our approach, the latent variables carry a linguistic interpretation, and are at least partially observed.


Of particular relevance are applications of neural architectures to PDTB implicit discourse relation classification Zhang et al., 2015;Braud and Denis, 2015).

the discrete latent variables in our model are easy to sum and maximize over.


As described in Section 2, the only previous attempt at automatically deriving the meaning of phonesthemes is due to Abramova et al.

our second similarity test is a check on the overall semantic cohesiveness of the candidate phonesthemic clusters.


An overview of the results can be seen in Figure 1.

in the first part of our study (Section 4) we present a stricter validation method for candidate phonesthemes that also includes considerations related to morphological diversity, which were ignored in previous work.


Hence, we would expect a negative correlation between cluster size and semantic similarity.

our phonestheme validation procedure is stricter compared to previous work since we use sets of words that share a random two-consonant prefix as baseline and, importantly, take into account morphological relatedness.


We also include features indicating the relation of the typed dependency of the chunks.

since certain adjacent words tend to be discarded or kept together, we reinforce this property by adding a bigram POs feature of w h to encode its context.


496,567 tokens on the target side) and about 2,691 pairs of parallel sentences for testing (approx.

i suggest you visit first the cathedral of " Le UNK UNK " because it is the most UNK building in the area .


As a first step towards developing full-fleged learning systems that leverage all signals available within a communicative setup, in our extended model we incorporate information regarding the objects that caregivers are holding.

it could be that MSG has simply learned to treat, say, the lamb visual vector as an arbitrary signature, functioning as a semantically opaque ID for the relevant object, without exploiting the visual resemblance between lamb and sheep.


To use maximum likelihood or minimum cross-entropy, it assumes that the model distribution is peaked.

we notice that, in ondevice model, user queries tend to be short, with on average 2.4 words in a query, shown in Table.


Different from RCRFs and conventional RNNs that in essence apply the multinomial logistic regression on the output layer, RSVMs optimize the sequence-level max-margin training criterion used by structured support vector machines (Tsochantaridis et al., 2005) on the output layer of RNNs.

model training can be sped up by skipping the weight updating for non-support vector training samples.


6 Both the development and test sets were annotated by a human judge and an author of this paper.

it is worth noting that the DDoS attack on RBS, Ulster Bank and Natwest was actually on 2015 July 31.


All experiments were run with the default settings except for a distortion-limit of 12 in the JP-EN experiment, as suggested by (Goto et al., 2013).

to put these results in the broader context of machine translation research, our approach (even without special handling of unknown words) achieved gains of up to 5.6 BLEU points over strong phrase-based and hierarchical phrase-based Moses baselines, with the help of an ensemble technique.


To overcome this issue, we propose an agreement model for neural machine translation and show its effectiveness on large-scale Japaneseto-English and Chinese-to-English translation tasks.

their approach was concerned with feedforward networks, which can not make full use of rich contextual information.


There has been little work on deception detection in written language and most of it has focused on either discriminating between sincere and insincere arguments (Mihalcea and Strapparava, 2009) or opinion spam (Ott et al., 2011;Jindal and Liu, 2008).

while their results are promising, our focus is on written text only.


The werewolves are motivated to hide their roles, as in every round there is a majority of non-werewolves.

hesitations (um, er, uh), hedges (sort of, kind of, almost), and polite forms are markers of powerless language (Sparks and Areni, 2008).


For simplicity, denote the entity set as E = {e 1 , • • • , e |E| }, and the template set as T .

many slot filling algorithms requires the full information of the event schemas and the labeled corpus.


By incorporating templates and slots as latent topics, probabilistic graphical models learns those templates and slots that best explains the text.

the edge weight between two points is their template level similarity / slot level similarity.


The most extensively developed resource for English is the MRC Psycholinguistic Database (Section 2).

the most extensively developed resource for English is the MRC Psycholinguistic Database (Section 2).


• Simple Ridge: Test word is assigned the property value as predicted by a Ridge regressor trained with the 15 aforementioned lexical features.

it is far from complete, most likely due to the inherent cost of manually entering such properties.


At the same time, we want to give special thanks to the anonymous reviewers for their insightful comments as well as suggestions.

all those works for document representation paid little attention to the variability of intra-topic documents.


For example, this approach was used by  and was originally used for PLTM.

figure 7: oPLTM vs. mlhPLTM: perplexity comparison (left); performance comparison on the CLIR task (right).


One alternative approach to longer documents that has received attention in the past has been to directly model local-i.e., Markov-dependencies among tokens.

the VB approach (Blei et al., 2003) offers more efficient computation but as in the case of Gibbs sampling requires iterating over the whole collection multiple times (e.g.


If a h ≥ 1.5 and a t ≥ 1.5, then r is labeled as M-M. 1.4%, 8.9%, 14.6% and 75.1% of the test triples belong to a relation type classified as 1-1, 1-M, M-1 and M-M, respectively.

our new KB completion model STransE chooses W r,1 , W r,2 and r so that W r,1 h + r ≈ W r,2 t. That is, a TransE-style relationship holds in some relation-dependent subspace, and crucially, this subspace may involve very different projections of the head h and tail t. So W r,1 and W r,2 can highlight, suppress, or even change the sign of, relation-specific attributes of h and t. For example, for the "purchases" relationship, certain attributes of individuals h (e.g., age, gender, marital status) are presumably strongly correlated with very different attributes of objects t (e.g., sports car, washing machine and the like).


Neural Network models like Convolutional Neural Networks and Recurrent Neural Networks (LSTM, GRU) have recently been been successfully used to tackle various sequence labeling problems in NLP.

the embeddings are trained on a large unlabeled biomedical dataset, compiled from three sources, the English Wikipedia, an unlabeled EHR corpus, and PubMed Open Access articles.


In this sentence, the true labels are Adverse Drug Event(ADE) for "bronchiolitis obliterans" and Drugname for "ABVD chemo".

(1994) showed that learning long term dependencies in recurrent neural networks through gradient decent is difficult.


More interestingly, we find that performance can be improved if the system scores and human ratings are aggregated over several topic cardinalities before computing the correlation.

2014proposed an automated approach to the word intrusion task.


Transition actions are treated as an atomic output component in each feature instance.

for scoring the action SHIFT-Lw-Lp, S 0 w is instantiated into S 0 w-SHIFT-Lw-Lp, where Lw is the word to shift and Lp is its POS.


To make quality vectors, we regard that the probability of the target word y j involves the quality information about whether the target word y j in target sentence is properly translated from source sentence.

feature selection is to select the best features by using selection algorithms, such as Gaussian processes (Shah et al., 2015) and heuristic (González-Rubio et al., 2013), among already extracted features.


7 From the extended prediction method of (4), the probability of the target word y j is computed by using information of relevant source words in source sentence x and all target words y y j surrounding the target word y j in target sentence.

by decoding t j , we are able to get quality vector q y j for the target word y j ∈ R Ky at position j of target sentence.


Word Embeddings for Verbs and Adjectives.

4 For all context types other than BOW we use the word2vec package of (Levy and Goldberg, 2014), 5 which augments the standard word2vec toolkit with code that allows arbitrary context definition.


Moreover, the SP-based model is much more compact than the alternatives, making its training an order of magnitude faster.

sG-sP and sG-Coor, which take 11 minutes and 23 minutes respectively to train, are substantially faster than the other w2v-sG models.


In order to tackle this problem, Scheirer et al.

we investigated the problem via reducing the open space risk, and proposed a solution based on cen-    ter-based similarity space learning.


We also tried gated recurrent units (GRUs) (Cho et al., 2014) and the basic RNN, but the results were generally lower than LSTM.

naive Bayes: (Lendvai and Geertzen, 2007).


we treated 252 neurons in the final hidden layer as dedicated neurons in weight initialization.


According to the structure shown in Figure 1, W 1 is a matrix of weights that is updated during training, thus the distributed representations contained in W 1 are learned simultaneously with the training of BLSTM-RNN on any supervised learning tasks.

recently, many state-of-the-art systems of tagging related tasks are implemented with bidirectional long short-term memory (BLSTM) recurrent neural network (rNN), for example, slot filling (Mesnil et al., 2013), part-of-speech tagging (Huang et al., 2015), and dependency parsing (Dyer et al., 2015) etc.


The matrix factorization family only uses the statistics of co-occurrence counts, disregarding of the position of word in sentence and word order.

it should be noted that the speed of our approach is acceptable compared with previous neural network language model based methods, including (Bengio et al., 2003;Mikolov et al., 2010;Mnih and Hinton, 2007), as our model uses a much simpler output layer which only has two nodes, avoiding the time consuming computation of the big softmax output layer in language model.


Therefore in bi-directional RNNs, not only a history vector of word w t is regarded but also a future vector.

with our neural models, we achieved new state-of-the-art results on the SemEval 2010 task 8 benchmark data.


We re-implemented the approach by Pichotta and Mooney (2014) with the exception that we use v(e subj , e dobj , e iobj ) instead of v(e subj , e obj , e prep ) to represent events.

for future work, this lack of coverage could be compensated for by backing off to the P&M model.


We can then write unary Thus we can regard the bilinear form as a function computing a weighted inner product over some real embedding v y U representing state y and some real embedding representing input factor t. The rank of W gives us the intrinsic dimensionality of the embedding.

within this context, we are interested in the task of predicting semantic tuples for images.


Interestingly, in 36% of games, the team arrives at a better answer than any of the individual guesses (c best > 0).

for example, the conversation in figure 4 has two confident phrases, underlined in red.


One possible clue as to why vision is better at predicting a concept's properties is given by the fact that it obtains better results on concepts such as PANTS or STOOL, where the only difference to very similar concepts like TROUSERS or CHAIR are visual (a STOOL has no backrest as opposed to a CHAIR).

table 2 shows some examples for each of the 10 property types as defined and annotated in MCRAE.


The published dataset contains a total of 2526 features, with a mean of 13.7 features per concept.

close-to-perfect performance in this task is impossible, since almost 30% of the features only occur with one concept, and hence can't be reconstructed for that particular concept.


Cross-lingual Wikification is the task of grounding mentions written in non-English documents to entries in the English Wikipedia.


We choose the CCA-based model because we can obtain multilingual word and title embeddings for all languages in Wikipedia without any additional data beyond Wikipedia.

they only focus on English Wikification.


Previous work in the area have proposed a number of methods for identifying and extracting task knowledge from search query sessions (Mehrotra and Yilmaz, 2015b;Wang et al., 2013;Lucchese et al., 2011;Verma and Yilmaz, 2014;Mehrotra and Yilmaz, 2015a).

in comparison, however, the baseline methods failed to identify diagnostic clusters.


The Z r , Z t (f ) and Z n (f ) terms denote the partition functions of each model, and φ r , φ n and φ t are functions mapping nonterminals and production rules to feature vectors.

we iteratively developed these templates to cover the data set, and the lexicon generated by these templates can correctly parse 96% of the examples in FOODCHAINS.


The suffix-prefix-based approach results to Bundes-finanz-ministerium and the prefixsuffix method to Bund-esfinanz-ministerium.

our method does not require any linguistic knowledge and is initialized using a large monolingual corpus.


We have examined schemes of priority ordering for integrating information from different candidate sets, e.g.

most existing systems rely on dictionaries or are trained in a supervised fashion.


The latter automaton uses integers as state names: it is 0 xn −→ n .

it removes γ i:j , e x i and e x j+1 and in fact, it is precisely a weighting of the edits in our original FST F , without further considering the context in which an edit is applied.


In an error analysis, it turned out that FF2010 (i.e., SMOR) cannot process 2% of the gold samples.

section 4 shows two efficient and flexible data structures used for our statistical compound splitter, which is described in section 5.


To conduct our experiments, we follow the single mode setting of Hermann et al.

to visualize the difference in embeddings learned with BRAVE-S and BRAVE-D, we selected sentiment words and identified crosslanguage nearest neighbors in table 5.


First, we experiment with the model for joint learning of two classifiers coupled with thread-level inference (Section 3.1).

we give a pseudocode in Algorithm 1 that trains this model in an online fashion using feedback from the loopy belief propagation (LBP) inference algorithm (to be described later in Section 3.1.1).


The dataset is split into training, development and test sets, with 2,600, 300, and 329 questions, and 16,541, 1,645, and 1,976 answers, respectively.

subsequent work has further merged Potential under BAD ( , and has used for evaluation F 1 with respect to the Good category (or just accuracy).


We also included some rules to consider special anonymization tokens in the SMS dataset (Chen and Kan, 2013).

in this work, we focus on tackling these issues while making the following two main contributions: • We build a new corpus of SMS data that is fully annotated with noun phrase information.


All models were built by us using Java, and were optimized with L-BFGS.

the texts are highly informal and noisy, with misspelling errors and without grammatical structures.


Only exact matches are considered correct.

this in turn will give us new and more realistic data which we can use to extend the corpus and to improve the semantic parser.


Second, we show that a parser read off the corpus achieves promising parsing accuracy and can be used to adapt SMT to multilingual database access.

issuing a query that is executable against the OSM database still requires detailed knowledge of database internals, something that cannot be expected from a layman user.


In the disC-trained models, the error signal within a time step (i.e.

table 1 displays some statistics of our dataset.


DENSIFIER differs from this work in that it does not need a text corpus, but can transform existing, publicly available word embeddings.

we then perform stochastic gradient descent (SGD).


This suggests that a single dimension is sufficient to encode all sentiment information needed for sentiment lexicon creation.

for frequency, we exploit the fact that word2vec stores words in frequency order; thus, the ranking provided by word2vec is our lexicon resource for frequency.


Simple morphological features ( §4.4) also seem to be meaningful.

for that, we consider the view annotation in the MPQA corpus.


We also add problem-specific constraints that specify which clusters cannot be merged together, but instead of manually creating the cannot-links between specific words, our cannot-link constraints are automatically calculated during the clustering process.

a few methods have been proposed to identify implicit features, e.g., using co-occurrence associations between implicit and explicit features (Su et al., 2006;Hai et al., 2011;Zhang and Zhu, 2013), or leveraging lexical relations of words in dictionaries (Fei et al., 2012).


paraphrases and semantic classes of heads and modifiers, but also proposed useful new features tailored to our task.

for example, heads having the hypernym Verbrechen (crime) are typically contained in compounds whose modifiers represent neither a holder nor a target, such as Steuervergehen (tax offense) or Autodiebstahl (car theft).


As an implementation, we use SVM light (Joachims, 1999).

the aim has been to predict verbs for those compounds that match those abstract relations (e.g.


(The Pearson correlation coefficient between the two sets of sentiment scores for each lexicon was also at least 0.98.)

the created lexicons capture sentiment associations at a fine level of granularity.


The annotators were presented with four terms at a time, and asked which term is the most positive (or least negative) and which is the most negative (or least positive).

as the difference in sentiment starts getting larger, the frequency with which the two terms are chosen as most positive begins to diverge.


Such a verb resource can be useful to aid KB relation extraction.

3), where we do not incorporate constraints and classify each test instance into a single relation.


Then, we apply convolution and pooling to each of the contexts separately.


the extraction of structured information from natural language text is challenging because one relation can be expressed in many different ways.


Parallel approaches do exist (Snoek et al., 2012;González et al., 2015), but we find it easy enough to harness parallel computation in decoding tuning sets and by decoupling BLEU measurements from speed measurements.

the maximum number of hypotheses allowed to survive histogram pruning in each decoding stack.


We address this by distributing computational paths according to different translation pairs over multiple GPUs, following (Ding et al., 2014).

since this attention mechanism was introduced to the encoder-decoder network for machine translation, neural machine translation, which is purely based on neural networks to perform full end-to-end translation, has become competitive with the existing phrase-based statistical machine translation in many language pairs Gulcehre et al., 2015;Luong et al., 2015b).


So far we have considered a conditional model of the target given the source, modelling p(t|s).

these correspond to local diagonal alignments or one-to-many alignments, respectively.


These sums represent the total alignment score for the surrounding source words, similar to fertility in a traditional latent variable model, which is the sum over binary alignment random variables.

it is well established for latent variable translation models that the alignments improve if p(s|t) is also modelled and the inferences of both directional models are combined -evidenced by the symmetrisation heuristics used in most decoders , and also by explicit joint agreement training objectives (Liang et al., 2006;Ganchev et al., 2008).


All of the exposition and results in this paper use this factorization, though many of the techniques we present later could be applied easily to the other factorizations described in .

indeed, in Section 5.3 we present quantitative and qualitative analysis of our results which further confirms this hypothesis: the LSTM and USchema models each perform better on different pattern lengths and are characterized by different precision-recall tradeoffs.


It assumes that all vectors of entities and relations lie on a single vector space.

in Figure 1-(a), e 1 , e 2 , and e 3 are placed linearly.


That is, when a triple (h, r, t) is given, h and t plays different roles.

for this, it introduces projection vectors for entities and relations, and then constructs the mapping matrices by multiplying these entity and relation projection vectors.


Due to the simplicity of role-specific projection of entity vectors, it can be applied to various translation-based embeddings.

the existing embeddings treat them equally and embed them into a space in the same way.


For this purpose, we adopt a head and a tail space mapping matrices of M h ∈ R n×n and M t ∈ R n×n .


We also aim for the goal that similarity values of all found important word in- teractions should be maximized.

neural networks and distributed representations can alleviate such sparsity, thus neural network-based models are widely used by recent systems for the STS problem (He et al., 2015;Tai et al., 2015;Yin and Schütze, 2015).


We are grateful for support from NSF Award 1464553 and the DARPA LORELEI Program.

our model has two main parts: an encoder and a decoder.


(source) language-independent, emphasising general effects of the process of translation.

(2013), we estimate surprisal in three ways, at the word, part-of-speech and syntax levels, based on ngram language models and language models trained on unlexicalised part-of-speech sequences and flattened syntactic trees.


While we describe how Translationese and Interpretese are different and characterize how they differ, the contribution of our work is not just examining an interesting, important dialect.

our hypothesis is that tactics used by interpreters roughly fall in two non-exclusive categories: (i) delay minimization, to enable prompt translation by arranging target words in an order similar to the source; (ii) memory footprint minimization, to avoid overloading working memory by reducing communicated information.


The input layer consists both source and target language word, which is in one-hot representation.

(3) If current source/target word is not aligned to any target/source words, we introduce a null token in its opposite side, and annotate this word pair as f ollow reordering type.


In order to include more context information for determining reordering, we propose to use a recurrent neural network, which has been shown to perform considerably better than standard feed-forward architectures in sequence prediction (Mikolov et al., 2011).

for other orientation types, such as LR and MSLR are also widely used, whose definition can be found on Moses official website 1 .


Then we employ an n-gram language model (LM) to score these candidates and select the one with the lowest perplexity as final result.

another option is to pre-process the input sentence by inserting possible DPs with the DP generation model (Section 2.2) so that the DP-inserted input (Input ZH+DPs) is translated.


For example, Chen and Ng (2013) propose an SVM classifier using 32 features including lexical, syntax and grammatical roles etc., which are very useful in the ZP task.

the general method is to make the input with N -best DPs into a confusion network.


Above, u and v are the parameters of the model, and h a and h p are learned feature embeddings of the local mention context and the pairwise affinity between a mention and an antecedent, respectively.

yet, state-of-the-art performance can be achieved with systems treating each mention prediction independently, which we attribute to the inherent difficulty of crafting informative clusterlevel features.


Singleton detection examines whether a phrase belongs to a coreference chain regardless of being anaphor or antecedent.

it is a very challenging task in natural language processing and it is still far from being solved, i.e.


With different approximation methods, (4) will have different equivalent forms, e.g.

using only word embeddings is not sufficient to represent complex lexical features (e.g.


Finally, in the TOEFL benchmark, all contexts except for SUB, perform comparably.

13 3) Coreference Resolution (COREF) We used the Berkeley Coreference System (Durrett and Klein, 2013), which achieves near state-of-the-art results with a log-linear supervised model.


Since the size of English vocabulary W may be up to 10 6 scale, hierarchical softmax and negative sampling (Mikolov et al., 2013b) are applied during training to learn the model efficiently.

moreover, Chinese characters are more ambiguous than words.


We propose a similaritybased method to learn Chinese word and character embeddings jointly.

using CBOW to learn Chinese word embeddings directly may have some limitations.


In this work, we advocate another approach and show that it is simpler and more effective to ignore unattached words and many-to-many alignments: we claim that training a parser from a corpus of highquality annotated (albeit partially) data will result in better parsing performances than a parser trained from fully-annotated but noisy data.

this solution comes at the expense of deleting words or creating fake dependencies in the target sentence, which may introduce unreliable annotations in the target data.


Our results so far are sobering: shortly after a static model is deployed performance degrades to a model using two orders of magnitude less training data (compare the drop in §6.2 with Figure 1).

the hour of the day has much more significant impact on accuracy; some times of the day are significantly easier and harder than the average.


Therefore, we conclude that the gains we obtained through interlocking the phrases, could not have been obtained by simply increasing the amount of searching performed by the baseline system.

their experiments on translating Arabic-English text from the news domain were encouraging.


This supports the hypothesis that reading patterns can help to distinguish good from bad translations.

combinations with BLEU When we combined BLEU with the translation jumps, we observed an increment in the τ to 0.37.


One of the issues arises from inexact algorithms adopted in order to solve the hard joint search problem.

if we use the existing parsers to only predict unlabeled trees, we also obtain speed improvement, even for the highly speed-optimzed Stanford Neural Parser.


This framework looks appealing in order to test our assumption that segmentation and parsing are mutually informative, while leaving the exact flow of information to be learned by the system itself: we do not postulate any priority between the tasks nor that all attachment decisions must be taken jointly.

the good scores for Sequoia could be explained by the larger MWE coverage.


Such a lexical analysis is particularly relevant to perform deep semantic analysis.

the lexical dimension tends to help syntactic predictions.


Word-sentiment associations are commonly captured in sentiment lexicons.

the oracle 'majority label' baseline assigns to all instances the most frequent polarity label in the dataset.


Our improvements are primarily due to better performance on unseen words.

we do not use this data for training, but only for evaluation, so our experiments use unsupervised (or weakly supervised) domain adaptation.


Using unlabeled data to estimate a target distribution for importance sampling, or for semi-supervised learning (Søgaard, 2013), as well as wide-coverage, crowd-sourced tag dictionaries to obtain more robust predictions for out-of-domain data have been succesfully used for domain adaptation Hovy et al., 2015a;Li et al., 2012).

we also see an average increase in performance on known words of 1% for both systems.


Both extracted bitext sets also contained many duplicate sentence pairs.

this incurs a computational cost that could be significant for large collections such as Gigaword.


More specifically, we use the formulaic similarity between He and Eu: He(p, q) ≡ Eu(x, y), when ∀i : i = 1, n of x i and y i , x i = √ p i and y i = √ q i , and compute He distance using Eu based, approximate NN computation approaches such as k-d trees 1 (Bentley, 1975).

the primary experimental comparison that we perform is between no bitext at all and a system trained with some bitext.


SMT systems generate scored candidates and select a sentence having the highest score as the translation result.

in the experimental results of system combination , recall increases but precision declines with respect to original SMT results.


(R. Riggs) We have analyzed stylistic patterns in quotations.

(A. Einstein) Cross-Sentence Conjunction: For both Quotations 1 and Non-Quotes 1, the high-level syntax feature with the highest χ 2 value was CC + NP + VP + ..


News: The vice president threatened action against any who question the legality of delaying the swearing-in of President Hugo Chvez, who is still in Cuba.

by incorporating the crossgenre knowledge to tweets, we are able to formulate the task of event extraction on tweets as the task of cross-genre extraction for tweets and news articles.


Our approach is implemented as a single stateful feature function in Moses (Koehn et al., 2007), which we will submit back to the community.

we experiment with word penalties based on either morphemes or desegmented words.


We choose to train GloVe (Pennington et al., 2014) vectors on a corpus comprised of Gigaword and Wikipedia to learn dense representations of 2000 dimensions for English and French.

we review the monolingual models, before introducing our novel bilingual formulation.


Some MCI patients may even recover, but all AD patients transition through the MCI stage before developing frank dementia (Petersen et al., 2001).

the web site offers a basic interface to display the scans and to transcribe their contents.


The size of the longest and shortest sentences, min-max sentence length, were used as features as well as the average of the length of all sentences occurring in the description.

such tests may be insensitive to early linguistic decline, when anomalies are already detectable by patients' families (Key-DeLyria, 2013).


When ensembleing very similar runs, such as runs submitted by the same team, the diversity of the systems is compromised and may lower the ensemble quality.

the 0-hop query from which it was derived must both exist and be correct.


(2015), which is neither as fast nor as easy to implement.

nCE in its standard form is not suitable for GPUs, as the computations are not amenable to dense matrix operations.


MRR is calculated by: where S is the set of similes.

we decided not to use these candidate generation methods.


See Table 1 for the query terms with the most albums returned.

automatic evaluation metrics are useful to quickly benchmark progress.


But the uses only need to perform one type of actions, which might be more suitable to be performed by a single human translator.

this result demonstrates that picking the critical error to be revised is critical in our PR framework.


The stack method can be quite costly, given that it increases the size of several matrices, either in the recurrent unit (for input) or the output mapping for word generation.

this information can be highly informative, for instance, keywords, titles or descriptions, often include central topics which will be helpful in modelling or understanding the document text.


We define context words to mean the surrounding words of a given word.

our training data for word embeddings is Wikipedia for English, downloaded on November 29, 2014.


The average number of embeddings for a word is 1.86 for K = 10.

we propose to exploit only context information to distinguish different concepts behind words in this paper.


To compute the similarity between the vector representation of the input sentences, our network uses two methods: (i) computing the similarity score obtained using a similarity matrix M (explored in (Yu et al., 2014)), and (ii) directly modelling interactions between intermediate vector representations of the input sentences via fully-connected hidden layers (used by (Hu et al., 2014)).

to the best of our knowledge, two of the most effective methods for engineering features are: (i) kernel methods, which naturally map feature vectors or directly objects in richer feature spaces; and more recently (ii) approaches based on deep learning, which have been shown to be very effective.


4.1) as the feature vector of SVM and + means that two embeddings were concatenated into a single vector.

the latter typically requires considerable effort especially when dealing with highly semantic tasks such as QA.


It would be interesting to see how such states capture the meaning of questions.

the encoders pre-trained in this manner are subsequently fine-tuned according to the discriminative criterion described already in Section 3.


When comparing between our models, Figure 2 shows that Transfer+EM consistently improves over the Direct Transfer, while the gains are more profound in the low-supervision scenario.

we select 14 prototypes (the most frequent word from each category) for the baseline, while our method only uses ten translation pairs.


Among the two baseline systems, MEMM performs slightly better than SVM, showing a small benefit to structured prediction.

there are three broad types of templates: five lexical feature templates, eight affix feature templates, and three orthographic feature templates.


As expected, the Early Modern English dataset (PPCEME) is considerably more challenging than the Modern British English dataset (PPCMBE): the  baseline accuracy is 7% worse on the PPCEME than the PPCMBE.

many of the most frequent errors on in-vocabulary (IV) tokens are caused by mismatches in the tagsets or annotation guidelines, and may be difficult to address without labeled data in the target domain.


SCL performs slightly better than Brown clustering and word2vec on IV tokens, but worse on OOV tokens.

to the PTB, there is no distinction between opening quotation mark and closing quotation mark in the PPCEME.


The arithmetic mean of 50 samples after 5,000 iterations, with an interval of 100 iterations.

our idea is materialized in two Bayesian generative models.


We show that an SVM classifier fails to separate creoles from non-creoles.

these features can be regarded as (statistical) universals  table 3: top-5 feature types for each source according to per-feature factors of FACt.


In his Bayesian generative model, each feature of a language has a latent variable which determines whether it is derived from an areal cluster or the tree.

in this way, we represent a first step toward understanding the complex process of creole genesis through statistical models.


Second, we propose to model creole genesis with mixture models, which makes more sense than tree-building techniques.

this feature value only gained the ratio of 67.3%.


This implies heterogeneous behavior of features in creole genesis.

we obtained 64 creoles and 541 non-creoles.


AFFIXES: prefixes and suffixes of the word.

language identification and normalization are critical for POS tagging , which in turn is critical further down the pipeline for shallow parsing as evident in Table 5.


5 Next, ignoring any language-specific factors, we would expect to observe a trend according to which the larger the corpus, the higher the correlation score.

we believe that this, in combination with word similarity tasks from the previous section, can give a reliable picture of the generic quality of word embeddings studied in this work.


2015assume their sense graph to be an ontology, this graph can be based on any inventory of word-sense and sense-sense relationships.

), based on evidence that this model shows consistently strong performance on a wide array of tasks Levy et al., 2015).


First, many essay-level within-task constraints are not enforced.

under Exact matching OUR system's performances on the RI-P and RI-R metrics are significant (p < 0.02), while under Approx matching, they are highly significant (p < 0.002).


The reason is that we apply distant supervision to derive a robust resource from the metadata of debate portals only.

thereby, our approach serves as a starting point for bringing argumentation mining to practical applications like search engines.


In (Wachsmuth et al., 2015), we studied the generality of sentiment-related argumentative structures across domains.

such approaches neither tend to be effective on documents from other domains, nor do they scale to applications that deal with huge document collections, such as search engines.


Aside from debate portals, one such resource is given by Wikipedia talk pages.

their approach concentrates mainly on exploiting the debate portals for improving the classification of segment roles, with minor impact on argumentativeness.


Afterwards the similarity is calculated as the cosine similarity between the block vectors.

while limited contextual features such as revision location have been utilized in prior work, such features are computed from the revision being classified but typically not its neighbors.


Additional examples are provided in Table 3.

in general, positive interpretations generated from numbered roles (ARG 0 -ARG 4 ) are scored higher than the ones generated from modifiers (ARGM-ADV, ARGM-CAU, ARGM-DiR, etc.).


We assigned 20% of instances to the test split, totalling 378 instances (Section 6).

obtain an accuracy of 65.5 using supervised machine learning and features derived from gold-standard linguistic information, and Blanco and Moldovan (2014) report an F-measure of 64.1.


This suggests that when the hypothesis is an entailment of the premise, the mLSTM tends to forget the previous matching results.

we observe that good wordlevel matching results are generally "forgotten" but important mismatches, which often indicate a contradiction or a neutral relationship, tend to be "remembered."


For α 2 , x is the frequency of r in training data, with k = .5.

exact inference will become intractable and we would need to resort to methods such as variational inference or sampling.


Our Contribution: We propose an extension of the distributional idea for unsupervised iSRL to loosen the need for annotated training data.

we label the first one with SEMAFOR 1 , a FrameNet-style semantic parser.


Hence, we introduce attention mechanism to extract such words that are important to the meaning of the sentence and aggregate the representation of those informative words to form a sentence vector.

w it with t ∈ [1, T ] represents the words in the ith sentence.


We carry out experiments with three different classification methods: SVMs with averaged embeddings, the Convolutional Neural Network of Kim (2014), and a Long Short Term Memory recurrent neural network (Hochreiter and Schmidhuber, 1997).

all hyperparameters of the network are the same as used in the original paper (Kim, 2014): stochastic dropout (Srivastava et al., 2014) with p = 0.5 on the penultimate layer, 100 filters for each filter region with filter regions of width 2,3 and 4.


Previous best classification results on TREC data is achieved by SVM using unigrams, bigrams, whword, head word, POS tags, hypernyms, WordNet synsets and a bunch of hand-coded rules.

the forget gate is able to decay the information according to the context rather than a fixed decaying weight in tensor product based CNNs.


Experiments show that DSCNN consistently outperforms traditional CNNs, and achieves state-of-the-art results on several sentiment analysis, question type classification and subjectivity classification datasets.

cNN based models, as the second category, utilize convolutional filters to extract local features (Kalchbrenner et al., 2014;Kim, 2014) over embedding matrices consisting of pretrained word vectors.


We consider three sets of word embeddings for our experiments: (i) word2vec 2 is trained on 100 billion tokens of Google News dataset; (ii) GloVe (Pennington et al., 2014) 3 is trained on aggregated global word-word co-occurrence statistics from Common Crawl (840B tokens); and (iii) syntactic word embedding trained on dependency-parsed corpora.

the model comprises k weight vectors w 1 , w 2 , ...w k , each of which is associated with an instantiation of a specific filter size.


While other three baseline methods all share the whole model between source/target domains but differ in the training schemes and performance.

there is no guarantee that two models having similar parameters yields similar output distributions.


For each task, we take the whole source domain training set D s and 10% sentences of the target domain training set D t as training data.

a linear combination is then applied to each label-wise MMD to form La-MMD and the coefficient is set as µ y = 1. regularization term is to generally control overfitting: We will provide the model convergence and hyperparameter study in Section 5.1.


With respect to the emission and transition score matrices E i,y i and A y i ,y i+1 , we adopt an upper bound between source/target domains, which helps the target domain predictor to be guided by the source domain predictor.

in this paper, we focus on medical NER from EHRs, which is a fundamental task and is widely studied in the research community (Nadeau and Sekine, 2007;Uzuner et al., 2011).


In practice, the difficulty of building a universally robust and high-performance medical NER system lies in the variety of medical terminologies and expressions among different departments of specialties and hospitals.

in this paper, we propose a novel NER transfer learning framework, namely label-aware double transfer learning (La-DTL): (i) We leverage bidirectional long-short term memory (Bi-LSTM) network (Graves and Schmidhuber, 2005) to automatically learn the text representations, based on which we perform a label-aware feature representation transfer.


Although these works consider both out-of-context noise and overly-specific noise, they rely on handcrafted features which become an impediment to further improvement of the model performance.

that work assumed that weights to control the hierarchical loss would be solicited from domain experts, which is inapplicable for FETC.


More precisely, a labeled corpus for entity type classification consists of a set of extracted entity mentions {m i } N i=1 (i.e., token spans representing entities in text), the context (e.g., sentence, paragraph) of each mention {c i } N i=1 , and the candidate type sets {Y i } N i=1 automatically generated for each mention.

we compute a probability distribution over all the K = |Y| types in the target type hierarchy Y.


In other words, config 9 (Table 5) is combination of both weighted negative and scaled positive extractions.

#out: count of output instances with cnfpi, Λ, Gq ¥ 0.5. avg: average.


We achieved this through SGD training of the diffeomorphism parameters θ, the means µ i of the Gaussian phones, and the parameters of the focalization kernel F. Our data is taken from the Becker-Kristal corpus (Becker-Kristal, 2006), which is a compilation of various phonetic studies and forms the largest multilingual phonetic database.

the essence of modeling is the same in that we explain formant values, rather than discrete IPA symbols.


We call models trained on datasets augmented with unlabeled corpus data or random strings DA-U or DA-R, respectively.

we thus conclude that multi-task training (instead of simple data augmentation) is crucial for the use of unlabeled data.


Also, we can notice that Wixarika has more unique words than the rest of our studied languages.

the last two questions are crucial: While for many languages it is difficult to obtain the number of annotated examples used in earlier work on (semi-)supervised methods, a limited amount might still be obtainable.


In order to make follow-up work on minimal-resource settings for morphological segmentation easily comparable, we provide predefined splits of our datasets 2 .

to the best of our knowledge, we are the first to try such an approach for a morphological segmentation task.


(2016) could be seen as the best alternative for translating into MRLs as it works at the character level on the decoder side and it was evaluated in different settings on different languages.

the look-up table includes high-quality affixes trained on the target side of the parallel corpus by which we train the translation model.


When the Turkish word 'terbiyesizlik' is generated, the first channel is supposed to predict t, e, r, up to k, one after another.

the second output channel helps us train better affix embeddings.


The f0 and E features are processed at the word level: each sequence of frames corresponding to a time-aligned word (and potentially its surrounding context) is convolved with N filters of m sizes (a total of mN filters).

figure 3 demonstrates one case where the pause feature helps in correcting a PP attachment error made by a text-only parser.


The NXT corpus provides reconciliation between Treebank and MS-State transcripts in terms of annotating missed/extra/substituted words, but parses were not re-annotated.

in the context of this conversation (the speaker was talking about another person in an informal manner), and everything acts more like filler -e.g.


Although we could in principle perform word discovery directly on speech, we leave this for future work, and only explore singletask and reconstruction models.

in the speech experiments, the decoders output the sequences at the grapheme level, so the output embedding size is set to 64.


In Table 2, we present results on three small datasets that demonstrate the efficacy of our models.

we didn't observe similar improvements in the text translation experiments.


Can the model identify vulnerable sentences, which are more likely to change the OH's view when addressed?

although this work did not look at the interaction between OHs and specific challengers, it provides valuable insight into persuasive arguments.


appeal to emotion requires some introspection and determining your own worth to your family etc.

some of these aspects have been used as features to predict debate winners (Wang et al., 2017) and view changes (Tan et al., 2016).


The intuition behind our model is that addressing certain parts of the OH's reasoning often has little impact in changing the OH's view, even if the OH realizes the reasoning is flawed.

in addition, 23% of the top pairs in one dimension capture the comment pointing out that the OH may have missed something (e.g., you don't know the struggles ...).


As expected, the frequency of changes in view differs across topics ( Figure 1b).

challenger 2's argument is relatively similar to the OH's reasoning, as it attempts to directly correct the OH's reasoning.


Similarly, the topological field (Höhle, 1986) identifying the major section of a sentence in relation to the clausal main verb is potentially relevant for a word's focus status.

if available in the original description of the approach, we also report the accuracy obtained without acoustic and prosodic features.


We have seen in section 5.2 that surface-based givenness is helpful in predicting focus.

this shows that while focus information is clearly useful in Short Answer Assessment, it needs to be reliable enough to be of actual benefit.


In the Shakespeare task, XU12 did observe a higher correlation with PINC (0.41) although the correlation was not with overall system ranking but rather only on the style metric.

meaning Preservation: modeling semantic similarity at a sentence level is a fundamental language processing task, and one that is a wide open field of research.


From Figure 4, we can see that our paragraph-level models (the latter three) overall outperform DU-pair baselines across all the subsets.

building Discourse Unit Representations: We aim to build discourse unit (DU) representations that sufficiently leverage cues for discourse relation prediction from paragraph-wide contexts, including the preceding and following discourse units in a paragraph.


However, nearly all the previous works assume that a pair of discourse units is independent from its wider paragraph-level contexts and build their discourse relation prediction models based on only two relevant discourse units.

third, implicit discourse relation prediction should benefit from modeling discourse relation continuity and patterns in a paragraph that involve easy-to-identify explicit discourse relations (e.g., "Implicit-Comparison" relation is followed by "Explicit-Comparison" in the above example).


We fixed the weights of word embeddings during training.

the multi-way classification setting is more appropriate and natural in evaluating a practical end-to-end discourse parser, and we mainly evaluate our proposed models using the four-way multi-class classification setting.


Our results indicate that the proposed utterance splitting applied to the training set greatly improves the neural model's accuracy and ability to generalize.

this can likely be attributed to the model having access to  table 6: Automatic metric scores of different models tested on the E2E dataset, both unmodified (s) and augmented (s) through the utterance splitting.


Although the E2E dataset contains a large number of samples, each MR is associated on average with 8.65 different reference utterances, effectively offering less than 5K unique MRs in the training set ( Fig.

in order to bring our model to produce more sophisticated utterances, we experimented with filtering the training data to contain only the most natural sounding and structurally complex utterances for each MR. For instance, we prefer having an elegant, singlesentence utterance with an apposition as the reference for an MR, rather than an utterance composed of three simple sentences, two of which begin with "it" (see the examples in Table 5).


regarding the "Listenability" evaluation, workers gave high scores to the Fine-tuned and Pseudo-melody models that are trained using both the melody and lyrics.


In the first example, the generator learns to complete the actions of placing the mixture into the a greased casserole and then baking it, which the MLE model misses.

indeed, recent studies have reported cases where commonly used measures do not align well with desired aspects of generation quality (Rennie et al., 2017;Li et al., 2016).


Although this was a CWI task, surprisingly only 4.7% of the words in the test data were identified as complex, and all the other words were viewed as simple.

coster and Kauchak (2011) employ a phrase-based Machine Translation system extended to support phrase deletion, and Wubben et al.


We collect the list of entity types of each entity in the FB5M through the predicate fb:type/instance.

the post processing step does not take into consideration that some verbs and prepositions do not fit in the sentence structure, or that some words are already existing in the question words (Example 4 table 7).


Automatic Metrics for evaluating text generation such as BLEU and METEOR give an measure of how close the generated questions are to the target correct labels.

for text generation from tables, (Lebret et al., 2016) extend positional copy actions to copy values from fields in the given table.


3.3) is another RNN that generates the output question.

we can see that it has affected the naturalness of the question.


Research in cognitive science, psychology and other social studies offer a great amount of work on (conscious and unconscious) biases and their effects on a variety of human activities (Kaheman and Tversky, 1972;Tversky and Kaheman, 1974).

our subjectivity lexicons are categorized into the following groups: Argumentation: This lexicon includes markers of argumentative discourse.


3 Using a trained neural network, we first project all published documents into a vector space such that a document tends to be close to its references.

(Caragea et al., 2014a,b;Lopez and Romary, 2010).


In this section, we delineate details of the process for collecting questions and answers.

figure 4 shows the count of first word(s) for our questions.


The numbers reported for this baseline represent the expected outcome (statistical expectation).

we present MultiRC (Multi-Sentence Reading Comprehension) 1 -a dataset of short paragraphs and multi-sentence questions that can be answered from the content of the paragraph.


To detect whether an essay is adversarial, we further augment the system with an adversarial text detection component that simply captures adversarial input based on the difference between the predicted essay and coherence scores.

our local coherence model is inspired by the model of Li and Hovy (2014) which uses a window approach to evaluate coherence.


Higgins and Heilman (2014) proposed a framework for evaluating the susceptibility of AES systems to gaming behavior.

the LSTM T&N and LC networks predict an essay and coherence score respectively (as described earlier), but now they both share the word embedding layer.


In addition to marking only a subset of the incorrect tokens at inference time, we also train new models for which the training data also only had a subset of incorrect tokens marked.

the system presents an initial translation to the user who can accept a prefix and select among the most likely postfix iteratively.


the sentence decoded from the initial system) and the reference sentence.

we consider the task of reformulating either a sentence, i.e.


According to Ganesalingam (2008), the sense of mathematical text is conveyed through the interaction of two contexts: the textual context (flowing text) and the mathematical (or symbolic) context (mathematical formulae).

scientific documents, such as those from Physics and Computer science, rely on mathematics to communicate ideas and results.


Moreover, DKRL fundamentally uses TransE (Bordes et al., 2013) method for encoding structural information.

in this work, we propose to enhance learning models with world knowledge in the form of Knowledge Graph (KG) fact triples for Natural Language Processing (NLP) tasks.


we report hypernym-specific scores -where the set of ground-truth edges considers just wordNet hypernyms -synonym-specific scores, and combined scores -where all wordNet hypernym and synonym edges are taken as ground truth, and a predicted edge must have the correct start node, end node, and relation type to be correct.


We conduct experiments aimed at addressing three primary research questions: (1) How does each taxonomic organization algorithm perform?

our second method selects hypernym edges for the taxonomy by using the Chu-Liu-Edmonds optimum branching algorithm (Chu and Liu, 1965;Edmonds, 1967) to solve the directed analog of the maximum spanning tree problem (DMST).


To understand why, we examine the output taxonomies.

how do DAG algorithms compare to tree-constrained ones, and how do transitive algorithms compare to their non-transitive counterparts?


As we can see in Figure 2, because of the alignment shift, both tied and fixnorm incorrectly replace the two unknown words (in bold) with But Deutsche instead of Deutsche Telekom.

this coincides with other observations that NMt's translations are often fluent but lack accuracy (Wang et al., 2017b;Wu et al., 2016).


It is more reasonable to have similar languages as auxiliary languages.

recent efforts (Artetxe et al., 2017;Conneau et al., 2018) also showed that it is possible to learn the transformation without any seeds, which makes it feasible for our proposed method to be utilized in purely zero parallel resource cases.


Effective conversation prediction and recommendation requires an understanding of both user interests and discourse behaviors, such as agreement, disagreement, inquiry, backchanneling, and emotional reactions.

the intuition is that messages of different discourse modes may show different distributions of the three word types.


For example, by leveraging both topical content and discourse structure, our model achieves a mean average precision (MAP) of 0.76 on conversations about the U.S. presidential election, compared with 0.70 by McAuley and Leskovec (2013), which only considers topics.

in addition to using 75% of conversation history, we also extract the first 25% and 50% of history as training.


While 41% arguments were categorized as abusive, other categories (tu quoque, circumstantial, and guilt by association) were found to be rather ambiguous with very subtle differences.

based on our observations, we summarize several linguistic and argumentative phenomena with examples most likely responsible for ad hominem threads in Table 4.


There are two strategies that we could use in deciding whether to align a scene graph node d (whose label space is O ∪ A ∪ R) with a word/phrase w in the sentence: • Word-by-word match (WBW): d ↔ w only when d's label and w match word-for-word.

we thank Peter Anderson, Sebastian Schuster, Ranjay Krishna, Tsung-Yi Lin for comments and help regarding the experiments.


7, selects a following transition, and updates the configuration.

at each step, we use a hinge loss defined as: where Y is the set of possible transitions and Y + is the set of correct transitions at the current step.


In the present work, the order of the tasks was inspired by cognitive and linguistic abilities (see § 1).

note that quantification always refers to animals (target set).


The model has a core structure, represented by layers 1-5 in the figure, which is shared across tasks and trained with multiple outputs.


We conduct a user study via Amazon Mechanic Turk (AMT) to test humans' performance on the datasets after they are remedied by our automatic procedures.

we examine our automatic procedures for creating decoys on five datasets.


recently, Lin and Parikh (2017) study active learning for Visual QA: i.e., how to select informative image-question pairs (for acquiring annotations) or image-question-answer triplets for machines to "learn" from.


On the contrary, a decoy may hardly meet QoU and IoU simultaneously 1 .

we conduct extensive empirical studies to demonstrate the effectiveness of our methods in creating better Visual QA datasets.


The decoys need to be plausible to the image.

the remedied datasets and the newly created ones are released and available at http://www.teds.


our design goal is that a learning machine needs to understand all the 3 components of an image-question-candidate answers triplet in order to make the right choiceignoring either one or two components will result in drastic degradation in performance.


The results suggest that the CNN prediction performance improves when word and context vectors are jointly learned by our attr2vec model.

note that there could be multiple rows in X referring to the same pair of words but associated with different contextual variables.


(2016), which learns word representations by adopting a ranking-based loss function.

moreover, note that our attr2vec algorithm, unlike GloVe, can handle generic contextual information.


In Section 4, we present the experimental results, and close this paper with some concluding remarks in Section 5.

we considered two topics: general news stories (G) and sport news (SP O).


To the best of our knowledge, attr2vec is the first model that incorporates syntactic dependency relations in a co-occurrence counts based model (such as GloVe).

we associate with each variable v ∈ V a bias term b v ∈ R and a latent factor vector f v ∈ R d , where the dimensionality d of the latent factor space is a hyperparameter of the model.


All of the Spearman's ρ values are comparable to those found in Levy and Goldberg (2014) and Hamilton et al.

we visualize the time points where a node is zero in Figure 5.


In the original dataset, there are 200 words categorized into 17 classes.

the second component incorporates wordspecific information into our model.


(2016b) use attested shifts generated by historical linguists.

models were then evaluated on their ability to detect when those words changed.


In this section, we evaluate our model's ability to measure the speed at which a word is changing.

if the bins are too small, the synchronic models get trained on insufficient data.


To preserve DIH, we propose a novel word embedding method, distributional inclusion vector embedding (DIVE), which fixes the two flaws by performing non-negative factorization (NMF) (Lee and Seung, 2001)  where k I is a constant which shifts PMI value like SGNS, Z = |D| |V | is the average word frequency, and |V | is the vocabulary size.

a combination of unsupervised DIVE with the proposed scoring functions produces new state-of-the-art performances on many datasets in the unsupervised regime.


This paper presents a corpus and experiments to mine possession relations from text.

the third, fourth and fifth classifiers predict temporal anchors, i.e., classify pairs between which a possession holds-either alienable or controlinto before yes or before no, during yes or during no, and after yes or after no.


Labels per temporal anchor with respect to verb x (binary flags for before, during and after) and possession type are presented in Table 3.

our approach extracts possessions intuitive to humans when there is no specific possession cue (e.g., we extract a control possession from The [computer] y at work was slow, [I] x didn't get anything done).


Linear mapping, which is a standard component of NNs, has been applied successfully in many tasks.

the dimension m for S and T is a hyperparameter and must be determined prior to training.


In view of this tradeoff, our work here further advances unsupervised learning of sentence embeddings.

it relies on a Siamese neural network architecture to predict surrounding sentences, contrasting our simpler unsupervised objective.


The above baseline Multi achieves state-of-theart performance for multi-domain sentiment analysis (Liu et al., 2016), yet the domain indicator d i is used solely to select softmax parameters.

the results on dataset 1 are shown in table 2 (the results on Blitzer's dataset exhibit similar results and are omitted due to space constraints).


We address this issue by investigating a model that learns domain-specific input representations for multi-domain sentiment analysis.

an end-to-end memory network is further proposed by Sukhbaatar et al.


where θ ds is the set of domain-specific parameters including domain descriptors, attention weights and softmax parameters.

a descriptor vector is learned for representing each domain, which is used to map adversarially trained domaingeneral Bi-LSTM input representations into domain-specific representations.


One naive baseline solution ignores the domain characteristics when learning f .

the mini-batch size, the size of the vocabulary V , dropout rate, learning rate for AdaGra and λ for adversarial training are set to 50, 10000, 0.4, 0.5 and 0.1, respectively.


(2011) They extract rich targetdependent and target-independent lexical and syntactic features for classification.

the transitions generated at each step are discrete, making it difficult to train and propagate errors to update the model parameters.


For instance, if embedding of a word 'अ छा|achcha' is unknown we translate it in English as 'good', and use its word embeddings in place of the source word 'अ छा|achcha'.

in the first architecture (A1, Figure 2a), we concatenate extracted features of each word of an instance with the corre- sponding word representations and pass it through a LSTM network followed by dense and output layers.


Closest to our work are Yang and Cardie (2013) (Y&C) and Katiyar and Cardie (2016) (K&C).

7 holders that both models predict incorrectly (hard cases) are less frequently subjects or A0 roles (col. 2, rows 2-3).


that overall character and word-level edit distances roughly matched the edit distances between clean and noisy examples in our parallel seed corpus.

reverse noising yields significantly more convincing errors, but the edit distance between synthesized examples is significantly lower than in real data ( Figure 5).


The final context dependent token representation h (e) t is the concatenation of the forward and backward pass token representations: To obtain the final context dependent token representation c j at the decoding time step j, we take a weighted average over to- Bahdanau et al.

encoder: We use a bi-directional LSTM (Graves et al., 2005) with attention mechanism as our sentence encoder.


To test this, we plot MAP for our best self-training model and various QA baselines as we vary the proportion of labeled training set in Figure 3.

this procedure can be repeated as long as both the two models continue to improve.


based algorithm are in the supplementary material.

we found that combining the web-facing model of Talmor et al.


Most of them are derived from some important properties associated with each quantity.

we apply the attention mechanism to scan all hidden state sequence of body by the last hidden state of question to pay more attention to those more important (i.e., more similar between the body and the question) words.


4 The userlevel tweets consist of ≈10 million tweets from 5,191 users mapped to their user-level features.

it suggests that the users having intelligence below average are present oriented but they seem to have negative view of it.


Major studies on time have been done for event detection (Ihler et al., 2006;Batal et al., 2012;Sakaki et al., 2013) which are mainly of the subjective consent.

for example, the tweet "Hoping to have fun among my friends but wishing I were with you instead" has a future orientation but it is mis-classified into the past orientation.


Our contributions are summarised as below: • We introduce the sentiment dimensions in the human temporal orientation to infer the social media users' psycho-demographic attributes on a large-scale.

when we considered the sentiment dimension, we found that it was actually correlated with present orientation with negative sentiment.


We examined our proposal using a recently published dataset of production norms (Jouravlev and McRae, 2016) and confirmed when people were explicitly asked to recall thematically related words, their responses were more likely located within the context embedding space in the vicinity of the cues word embedding.

facilitation is seen for word pairs that are purely category coordinates (lawyersurgeon) or purely associates (scalpel-surgeon), and pairs that share both types of relations (nursesurgeon) tend to see an additive processing benefit that reflects the privilege of both similarity and relatedness, an effect generally referred to as the "associative boost" (Chiarello et al., 1990;Lucas, 2000).


Third, defining a semantic measure that does not require references avoids the difficulties incurred by their non-uniqueness, and the difficulty in collecting high quality references, as reported by Xu et al.

this places these measures in the 3rd and 4th places in the shared task, where the only two systems that surpassed it are marginally better, with scores of 0.33 and 0.34, and where the next


We further compute an average human score: Inter-annotator agreement rates are computed in two ways.

unlike in their model, SAMSA stipulates that not only should multiple events evoked by a verb in the same sentence be avoided in a simplification, but penalizes sentences containing multiple events evoked by a lexical item of any category.


We use 300D Glove 6 (Pennington et al., 2014) and 1000D wiki2vec 7 pre-trained vectors to initialize our word and entity vectors.

to this end, we present a method to effectively apply linked entities in sequence-tosequence models, called Entity2topic (E2t).